Monte Carlo Tree Search (MCTS) is a branch of stochastic modeling that utilizes decision trees for optimization, mostly applied to artificial intelligence (AI) game players. This project imagines a game in which an AI player searches for a stationary target within a 2-D lattice. We analyze its behavior with different target distributions and compare its efficiency to the Levy Flight Search, a model for animal foraging behavior. In addition to simulated data analysis we prove two theorems about the convergence of MCTS when computation constraints neglected.
@article{arxiv.2011.14246,
title = {Monte Carlo Tree Search for a single target search game on a 2-D lattice},
author = {Elana Kozak and Scott Hottovy},
journal= {arXiv preprint arXiv:2011.14246},
year = {2020}
}